Mohamad, M. S. and Omatu, S. and Deris, S. and Misman, M. F. and Yoshioka, M. (2009) A multi-objective strategy in genetic algorithms for gene selection of gene expression data. Artificial Life and Robotics, 13 (2). pp. 410-413. ISSN 1614-7456
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Official URL: http://dx.doi.org/10.1007/s10015-008-0533-5
A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classifi cation. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classifi cation. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classifi cation. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm.
|Uncontrolled Keywords:||cancer classification, genetic algorithm, gene expression data gene selection, multi-objective|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Nor Asmida Abdullah|
|Deposited On:||18 Jan 2011 07:26|
|Last Modified:||14 Feb 2017 06:19|
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